prediction mechanism
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Mobile applications are progressively becoming more sophisticated and complex, increasing their computational requirements. Traditional offloading approaches that use exclusively the Cloud infrastructure are now deemed unsuitable due to the inherent associated delay. Edge Computing can address most of the Cloud limitations at the cost of limited available resources. This bottleneck necessitates an efficient allocation of offloaded tasks from the mobile devices to the Edge. In this paper, we consider a task offloading setting with applications of different characteristics and requirements, and propose an optimal resource allocation framework leveraging the amalgamation of the edge resources. To balance the trade-off between retaining low total energy consumption, respecting end-to-end delay requirements and load balancing at the Edge, we additionally introduce a Markov Random Field based mechanism for the distribution of the excess workload. The proposed approach investigates a realistic scenario, including different categories of mobile applications, edge devices with different computational capabilities, and dynamic wireless conditions modeled by the dynamic behavior and mobility of the users. The framework is complemented with a prediction mechanism that facilitates the orchestration of the physical resources. The efficiency of the proposed scheme is evaluated via modeling and simulation and is shown to outperform a well-known task offloading solution, as well as a more recent one.

For facing of the problems caused by the YOLOv4 algorithm’s insensitivity to small objects and low detection precision in traffic light detection and recognition, the Improved YOLOv4 algorithm is investigated in the paper using the shallow feature enhancement mechanism and the bounding box uncertainty prediction mechanism. The shallow feature enhancement mechanism is used to extract features from the network and improve the network’s ability to locate small objects and color resolution by merging two shallow features at different stages with the high-level semantic features obtained after two rounds of upsampling. Uncertainty is introduced in the bounding box prediction mechanism to improve the reliability of the prediction of the bounding box by modeling the output coordinates of the prediction bounding box and adding the Gaussian model to calculate the uncertainty of the coordinate information. The LISA traffic light data set is used to perform detection and recognition experiments separately. The Improved YOLOv4 algorithm is shown to have a high effectiveness in enhancing the detection and recognition precision of traffic lights. In the detection experiment, the area under the PR curve value of the Improved YOLOv4 algorithm is found to be 97.58%, which represents an increase of 7.09% in comparison to the 90.49% score gained in the Vision for Intelligent Vehicles and Applications Challenge Competition. In the recognition experiment, the mean average precision of the Improved YOLOv4 algorithm is 82.15%, which is 2.86% higher than that of the original YOLOv4 algorithm. The Improved YOLOv4 algorithm shows remarkable advantages as a robust and practical method for use in the real-time detection and recognition of traffic signal lights.

International trade becomes increasingly frequent with the deepening of economic globalization. In order to ensure the stable and rapid development of international trade and finance, it is particularly crucial to predict the sales trend of foreign trade goods in advance through the network model of computer trade platform. To optimize the accuracy of sales forecasts for foreign trade goods, under the background of "Internet plus foreign trade", the controllable relevance big data mining of foreign trade goods sales, personalized prediction mechanism, intelligent prediction algorithm, improved distributed quantitative and centralized qualitative calculation are taken as the premise to design dynamic prediction model on export sales based on controllable relevance big data of cross border e-commerce (DPMES). Moreover, after the related experiments and comparative discussions, the forecast error ratios from the first quarter to the fourth quarter are 2.3%, 2.1%, 2.4% and 2.4% respectively, which are also within the acceptable range. The experimental results show that the design combines the advantages of openness and extensibility of Internet plus with dynamic prediction of big data, and achieves the wisdom, quantitative and qualitative prediction of the volume of goods sold under the background of "Internet plus foreign trade", which is controlled by the relevant data of foreign trade. The overall performance of this design is stronger than the previous models, has better dynamic evolution and high practical significance, and is of great significance in the development of international trade and finance.

The surprisal metric (Hale, 2001; Levy, 2008) successfully predicts syntactic complexity in a large number of online studies (e.g., Demberg and Keller, 2009; Levy and Keller, 2013). Surprisal assumes a probabilistic grammar that drives the expectation of upcoming linguistic material. Consequently, wrong predictions lead to a processing cost, presumably due to reranking related computations (Levy, 2013). Critically, surprisal assumes that the predicted parses generated by the probabilistic grammar are grammatical. However, it has been found that syntactic predictions can be ungrammatical (e.g., Apurva & Husain, 2018). Consequently, similar to reranking costs incurred due to incorrect (grammatical) predictions, a cost should also appear for ungrammatical predictions. Evidence for such a cost during comprehension will not be explained by the surprisal metric. To test the ecological validity of the surprisal metric, it becomes critical to investigate if ungrammatical predictions incur a cost. In this study, we investigate this issue in Hindi (a verb-final language) using a cloze task followed by a self-paced reading (SPR) study. All analyses were carried out in R using linear mixed models. Log RTs (reading time) were used for the RT analyses. In the cloze study (N=30), participants were asked to complete the sentences (such as 1a, 1b) meaningfully using the SPR paradigm. The two conditions differed in the case markers on the three nouns. 12 sets of experimental items along with 64 fillers were used. Participants’ responses were coded for the predicted verb class and the overall grammaticality of the completion (grammatical prediction vs ungrammatical prediction). 1a. hari-ne geeta-se umesh-ko…. Hari-ERG Geeta=ABL Umesh=ACC. 1b. hari-ko geeta-ne umesh-ko …. Hari-ACC Geeta-ERG Umesh-ACC. Grammaticality analysis of the completion data showed that participants make more ungrammatical completions in conditions (b) compared to (a) (z=5.25). The overall grammatical completions in condition (a) was 96% while in (b) it was 60%. In addition, the verb class analysis showed that in both conditions participants completed the sentences with a transitive non-finite verb followed by a ditransitive matrix verb (hereafter T.NF-DT.M) most frequently. T.NF-DT.M were predicted in 33% instance in condition (a) and 34% in condition (b) (z=0.18). Given the similar cloze probabilities, the surprisal metric will predict no difference in RT at T.NF-DT.M in the two conditions during online processing (cloze probabilities can be used to compute surprisal, see Levy and Keller, 2013). If the RTs at T.NF-DT.M in condition (a) is less than (b) that would be better explained by the higher cost due to the ungrammatical prediction. To ascertain this, we conducted an SPR study (n=50) using items similar to the ones used in the previous experiment (see, 2a and 2b). The critical region was T.NF-DT.M. 24 set of items along with 72 fillers were constructed. 2a hari-ne geeta-se umesh-ko milne ko kaha, Hari-ERG Geeta=ABL Umesh=ACC meet-inf(T.NF) told(DT.M) 2b hari-ko geeta-ne umesh-ko milne ko kaha , ... Hari-ACC Geeta=ERG Umesh=ACC meet-inf(T.NF) told(DT.M) While the prediction of T.NF-DT.M is the same in the two conditions, % ungrammatical predictions are more in (b) vs (a). Results show that the RT in (a) < (b) at the critical region (t=2.32). This goes against the surprisal metric and shows the cost incurred due to ungrammatical predictions. Our work establishes that the cost of ungrammatical predictions indeed appears during online processing. This processing cost is not predicted by a metric like surprisal and highlights its limitations. This study also provides evidence against the robust predictions in head-final languages. It suggests that the prediction mechanism in such languages is more nuanced and points to the need to study the nature of ungrammatical predictions during processing.